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An iterative energy-based multimodal transformer for joint retrieval of wheat soil moisture, leaf area index, and plant height from Sentinel-1 and Sentinel-2 time series

arXiv:2606.25174v1 Announce Type: cross Abstract: Field-scale retrieval of surface soil moisture (SM), leaf area index (LAI), and plant height (PH) is essential for precision agriculture, yet it remai

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Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis

arXiv:2606.25390v1 Announce Type: new Abstract: Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Ana

Approximate Structured Diffusion for Sequence Labelling

arXiv:2606.18856v2 Announce Type: replace Abstract: Sequence labelling, a core task of Natural Language Processing (NLP), consists in assigning each token of an input sentence a label. From a Machine

researcharxiv-cs-cl

Are Tabular Foundation Models Robust to Realistic Query Distribution Shifts in Microbiome Data?

model-releasesarxiv-cs-lg

arXiv:2606.24995v1 Announce Type: new Abstract: Tabular foundation models (TFMs) achieve strong performance on microbiome abundance data, yet their robustness under realistic distribution shift remain

Are We There Yet? Exploring the Capabilities of MLLMs in Assistive AI Applications

arXiv:2606.25084v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have redefined visual understanding by combining vision encoders with large-scale language models. This unified

ArteryX: A Reliable End-to-End Toolbox for Standardized Intracranial Artery Feature Extraction from 3D TOF-MRA

arXiv:2507.07920v2 Announce Type: replace-cross Abstract: Cerebrovascular research heavily relies on quantitative analysis of intracranial arteries from time-of-flight magnetic resonance angiography,

safetyarxiv-cs-cv

Applications

Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints

arXiv:2603.11606v2 Announce Type: replace Abstract: Building high-fidelity digital twins of articulated objects from visual data remains a central challenge. Existing approaches depend on multi-view c

applicationsarxiv-cs-cv

ARTOO-DARTU: Studying AR-HRC With AR Obstruction Mitigation During a Warehouse Task

arXiv:2606.25202v1 Announce Type: cross Abstract: Human-robot collaboration (HRC) often requires robot intentions and internal states to be conveyed to users for task efficiency and safety. Recently,

safetyarxiv-cs-ro

ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments

arXiv:2606.25207v1 Announce Type: cross Abstract: Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding

safetyarxiv-cs-cl

ASSCG: Just-Right Gating over Chattering for Fast-Slow LLM Planning in Autonomous Driving

arXiv:2606.25509v1 Announce Type: cross Abstract: Large language models (LLMs) can improve autonomous driving planning but are costly to query online, and existing fast-slow planners often rely on han

agentsarxiv-cs-cv

ATMA: Length-Invariant Language Modeling via Polar Attention and Gated-Delta Compression Memory

arXiv:2606.25156v1 Announce Type: new Abstract: Modern large language models based on softmax scaled-dot-product attention are constrained by their training sequence length: as the key-value sequence

local-aiarxiv-cs-lg

Attractive and Repulsive Pattern Control in Sequence Generation

arXiv:2606.24911v1 Announce Type: cross Abstract: Variable-order Markov models preserve local symbolic syntax by adapting context length, but long continuations can enter recurring high-order 'tunnels

researcharxiv-cs-ai

Auto-Configured Explainable Graph Neural Networks for Multi-Site Pollution Prediction

arXiv:2606.24978v1 Announce Type: new Abstract: Accurate particulate matter (PM) prediction is crucial for mitigating air pollution. Graph Neural Networks (GNNs) effectively model spatiotemporal depen

Auto-exploration for online reinforcement learning

model-releasesarxiv-cs-lg

arXiv:2512.06244v2 Announce Type: replace Abstract: The exploration-exploitation dilemma in reinforcement learning (RL) is a fundamental challenge to efficient RL algorithms. Existing algorithms for f

Auto-Labelling-Based Domain Transfer for 3D Object Detection on a Bicycle-Mounted LiDAR Platform

arXiv:2606.25652v1 Announce Type: new Abstract: Reliable 3D perception of vulnerable road users (VRUs) such as cyclists and pedestrians is essential for their safety in urban traffic and a core requir

Autodata: An agentic data scientist to create high quality synthetic data

arXiv:2606.25996v1 Announce Type: cross Abstract: We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show

agentsarxiv-cs-cl

Automatic Generation of Highlights for Academic Paper Via Prompt-based Learning

arXiv:2606.25253v1 Announce Type: new Abstract: Highlights provide a concise summary of the main contributions of an academic paper and help readers quickly understand its focus. However, many journal

local-aiarxiv-cs-cl

Applications

AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search

arXiv:2606.25871v1 Announce Type: cross Abstract: How can we generate high-quality relevance annotations at scale without the cost and delays of human labeling? Relevance annotations are the backbone

applicationsarxiv-cs-ai

BCoughBench: Benchmarking Respiratory Acoustic Foundation Models Under Body-Coupled Wearable Sensor Conditions

arXiv:2606.25116v1 Announce Type: cross Abstract: Respiratory acoustic foundation models (FMs) are benchmarked exclusively on smartphone recordings, yet clinical deployment increasingly targets body-c

researcharxiv-cs-ai

Benchmarking Deep Learning Models for Laryngeal Cancer Staging Using the LaryngealCT Dataset

arXiv:2510.11047v2 Announce Type: replace Abstract: Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep learning (DL) model development. We present Larynge

Benchmarking the Alignment of Data-Quality Metrics, Human Judgment and Land-Cover Segmentation Performance for Earth Observation

arXiv:2606.25128v1 Announce Type: cross Abstract: Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs

safetyarxiv-cs-cv

Beyond a Shadow of a Doubt: Close Proximity Geometry Reconstruction Using FMCW Radar Shadow Effects

arXiv:2606.25829v1 Announce Type: new Abstract: Reliable perception in adverse conditions remains challenging for autonomous systems, as cameras and LiDAR degrade in poor lighting or weather. Millimet

agentsarxiv-cs-ro

Applications

Beyond Defensive Reporting: Machine Learning for Active Anti-Money Laundering Control in Insurance

arXiv:2606.16663v2 Announce Type: replace Abstract: Money laundering through insurance claims poses a threat to insurers both through fraudulent payouts and reputational and regulatory risk. Despite t

applicationsarxiv-cs-lg

Beyond Function Calling: Benchmarking Tool-Using Agents under Tool-Environment Unreliability

model-releasesarxiv-cs-cl

arXiv:2606.25819v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments. Although recent tool-use benc

Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making

arXiv:2606.25421v1 Announce Type: new Abstract: Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. Howeve

safetyarxiv-cs-cl

Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors

model-releasesarxiv-cs-lg

arXiv:2606.25527v1 Announce Type: new Abstract: Online reinforcement learning (RL) agents increasingly depend on knowledge acquired offline to achieve practical efficiency. Originally studied in offli

Beyond Shapley: Efficient Computation of Asymmetric Shapley Values

arXiv:2606.25103v1 Announce Type: new Abstract: We address the problem of explainability in machine learning models through feature attribution methods. In particular, we consider a variant of Shapley

researcharxiv-cs-ai

Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning

arXiv:2512.00727v2 Announce Type: replace Abstract: Reinforcement learning has enabled impressive locomotion skills on articulated robots, but common policy representations remain only weakly aligned

safetyarxiv-cs-ro

Beyond Visual Forensics: Auditing Multimodal Robustness for Synthetic Medical Image Detection

arXiv:2606.25375v1 Announce Type: new Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud. Although prio

BFMTrack: Latent Sequence Optimization for Physics-Based Motion Tracking with Behavioral Foundation Models

arXiv:2606.25056v1 Announce Type: new Abstract: Behavioral Foundation Models (BFMs) offer a promising path toward universal physics-based character control by organizing a rich repertoire of physicall

safetyarxiv-cs-ro

Bias-Controlled Primal-Dual Natural Actor-Critic: Optimal Rates for Constrained Multi-Objective Average-Reward RL

arXiv:2606.25012v1 Announce Type: new Abstract: Many reinforcement learning (RL) problems in the infinite-horizon average-reward setting require optimizing multiple conflicting objectives while satisf

safetyarxiv-cs-lg

Bias Fitting to Mitigate Length Bias of Reward Model in RLHF

arXiv:2505.12843v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences. However, RLHF often

safetyarxiv-cs-lg

BiPACE: Bisimulation-Guided Policy Optimization with Action Counterfactual Estimation for LLM Agents

arXiv:2606.25556v1 Announce Type: new Abstract: Stepwise group-based RL is an attractive way to train long-horizon LLM agents without a learned critic: it reuses multiple sampled rollouts to estimate

local-aiarxiv-cs-cl

BitNet Text Embeddings

arXiv:2606.25674v1 Announce Type: new Abstract: LLM-based text embedders have substantially improved retrieval and semantic representation quality, but their deployment remains costly: large backbone

researcharxiv-cs-cl

Black-Box Assisted Regression: Phase Transitions and Minimax Optimality

arXiv:2606.25743v1 Announce Type: new Abstract: Foundation models are often used as fixed black-box predictors for downstream tasks with limited labeled data, but their predictions may be biased and u

Blasto-Net: An Explainable Multi-Task Learning for Blastocyst Segmentation, Grading, and Implantation Prediction

arXiv:2606.25463v1 Announce Type: cross Abstract: This study introduces Blasto-Net, a multi-task deep learning model for comprehensive blastocyst analysis. The proposed model performs three tasks simu

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

arXiv:2511.11421v2 Announce Type: replace Abstract: Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge. Vision-language models s

BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding

model-releasesarxiv-cs-ai

arXiv:2606.25400v1 Announce Type: new Abstract: Brain-Computer Interfaces (BCIs) and brain signal understanding are pivotal for clinical health and next-generation interactions. Despite this significa

Tutorials

Breaking Data Symmetry is Needed For Generalization in Feature Learning Kernels

arXiv:2604.00316v2 Announce Type: replace-cross Abstract: Grokking occurs when a model achieves high training accuracy but generalization to unseen test points happens long after that. This phenomenon

tutorialsarxiv-cs-lg

Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation

arXiv:2606.25432v1 Announce Type: cross Abstract: Inference efficiency is typically pursued by shrinking the model: distillation, pruning, quantization, and sparse routing each lower per-token cost wh

Bridging Spherical Black-Box Optimizers

arXiv:2606.25761v1 Announce Type: new Abstract: When gradient information is unavailable, black-box optimization (BBO) methods provide a practical alternative. While Evolution Strategies (ES), Consens

C2RM-Seg: Causal Counterfactual Reasoning with Structural-Semantic Priors for Weakly Supervised Histopathological Tissue Segmentation

arXiv:2606.25508v1 Announce Type: new Abstract: Histopathological tissue segmentation is essential for computer-aided diagnosis, yet weakly supervised methods often suffer from noisy pseudo-labels gen

local-aiarxiv-cs-cv

C3-Bench: A Context-Aware Change Captioning Benchmark

arXiv:2606.25445v1 Announce Type: new Abstract: While Change Captioning systems have garnered substantial attention to respond to our evolving world, their true performance on diverse real-world chang

Cage-based Texture Transfer with Geometric Filtering

arXiv:2606.25220v1 Announce Type: new Abstract: Real-time texture transfer expands the creative horizon for interactive applications, enabling seamless detail projection in scenarios that range from d

Calousel: Extrinsic Calibration of Non-overlapping Multi-camera Systems from Pure Rotation

arXiv:2606.25646v1 Announce Type: cross Abstract: Extrinsic calibration of multi-camera systems with non-overlapping FOVs has been a challenging problem in the robotics literature. Conventional target

Can Trustless Agents Be Trusted? An Empirical Study of the ERC-8004 Decentralized AI Agent Ecosystem

arXiv:2606.26028v1 Announce Type: cross Abstract: As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether

agentsarxiv-cs-ai

CARE: Controlling LLM-Generated Policies through Auditable Review of Evidence in Scientific Experimentation

arXiv:2606.14581v2 Announce Type: replace Abstract: Granting LLMs direct control over costly, irreversible scientific experiments leads to unsafe exploration and unstable performance, but discarding L

Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models

arXiv:2606.25473v1 Announce Type: new Abstract: Autoregressive video diffusion with causal diffusion transformers has emerged as a major paradigm for real-time streaming video generation and action-co

safetyarxiv-cs-cv